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Agents in a society can reach consensus, either agreeing with a social rule or opposing it. This consensus emerges over time, with agents initially ignoring neutral issues to adapt.

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Area of Science:

  • Statistical mechanics
  • Sociophysics
  • Agent-based modeling

Background:

  • Understanding consensus formation in social systems is crucial.
  • Societies comprise adapting, interacting agents influenced by social rules.
  • The role of interaction strength and social rules in consensus dynamics is complex.

Purpose of the Study:

  • To model the emergence of consensus in a society of adapting agents.
  • To investigate how social rules and agent interactions influence consensus.
  • To analyze the conditions under which consensus is reached or not reached.

Main Methods:

  • Developed a statistical mechanics model.
  • Employed the mean-field approximation for analysis.
  • Studied agent interactions (H₀) and temperature (T) effects.

Main Results:

  • Weak agent interaction leads to consensus aligned with the social rule (B).
  • Strong agent interaction can result in consensus against the status quo.
  • Agents initially neglect neutral issues before reaching asymptotic consensus.
  • A finite transient time (αₜ) for consensus is observed under most conditions.

Conclusions:

  • The strength of agent interaction is a critical factor in consensus formation.
  • Consensus dynamics are dependent on the interplay between social rules and agent behavior.
  • Specific conditions (H₀=1, T→∞, and a defined inequality region) prevent consensus attainment.